Nerve conduction studies (NCS) play a key role in the assessment of neuropathies, including focal entrapments such as carpal tunnel syndrome and lumbosacral radiculopathies, as well as for neuropathies resulting from diabetes and acquired inflammatory demyelinating polyneuropathies.
NCS are generally conducted by applying an electrical stimulus to a nerve. This electrical stimulation depolarizes a short segment of the nerve (whether a motor nerve or a sensory nerve) at the point of stimulation. If this electrical depolarization exceeds a certain threshold, an action potential impulse is initiated. This action potential impulse propagates along the nerve, both distally and proximally, from the point of stimulation.
In the case of a motor nerve, distally-propagating nerve impulses reach the muscle and depolarize the muscle fibers, typically causing a response or “twitch” in that muscle. This electrical activity of the muscle is measured as a compound muscle action potential (CMAP).
Proximally-propagating impulses reach the motor neuron cell bodies located in the anterior horn of the spinal cord. In a small, and random, fraction of the stimulated neurons, the neuron depolarizes again (i.e., it “backfires”), resulting in a new distally-traveling impulse (this is sometimes referred to as “back propagation”). The muscle responses resulting from these back-propagating impulses are generally referred to as F-waves.
F-waves travel through a longer segment of the nerve than CMAPs and are therefore more sensitive to systemic changes in the conduction properties of the nerve fibers and/or localized changes in the proximal segment of the nerve fibers. F-waves have been routinely used as a clinical measurement to provide useful diagnostic information in the evaluation of neuromuscular function and neuropathies.
The recorded muscle activities which result from an electrical stimulus are generally referred to as a response trace. The temporal segment of the response trace, which corresponds to the time frame of possible F-wave activities, is called an F-wave trace. FIG. 1 shows a set of five F-wave traces, marked as trace A through trace E.
Current approaches for using F-waves to evaluate neuromuscular function generally identify the time of the earliest F-wave activity in each trace (denoted as solid dots in FIG. 1), and define the onset time of this earliest F-wave activity, which is also known as the F-wave latency (FWL). For a given group of F-wave traces, the minimum, average and maximum FWL values (i.e., times) are calculated for that group of F-wave traces and defined to be the minimum FWL, the mean FWL and the maximum FWL parameters for that group of F-wave traces, respectively. These and other FWL parameters, such as F-wave persistence and chronodispersion, are frequently used by clinicians for multiple clinical applications. These clinical applications include (i) the detection of lumbosacral radiculopathy; (ii) the detection of dynamic changes associated with lumbar spinal stenosis; (iii) the early detection of axonal and demyelinating polyneuropathies; (iv) the demonstration of therapeutic response of pharmacological agents such as baclofen; and (v) the monitoring of changes in the motor neuron pool and central nervous system. Thus, determining FWL is frequently of significant interest to the clinician looking to assess neuromuscular function and/or treat neuropathies.
Another F-wave parameter which can be used to assess neuromuscular function is the time of arrival (TOA) of the F-wave components (F-wavelets). Each backfiring motor neuron produces a nerve impulse traveling along its own nerve fibers (i.e., its axons) before reaching the muscle fibers of the muscle group which is to be studied. The propagation time of each nerve impulse determines the TOA of an F-wavelet (a component of a complete F-wave complex) that may vary according to variations in axonal conduction velocity. For example, conduction velocity may vary due to partial or incomplete injury that affects only some, but not all, axons.
For any given stimulus, when only one motor neuron backfires, or when a few motor neurons backfire but they all have relatively close conduction speeds, one F-wavelet (with only one determinable TOA) may be formed. See, for example, FIG. 1, where F-wave traces C, D and E contain a single F-wavelet c, d and e, respectively.
However, when several motor neurons backfire with different conduction speeds, multiple F-wavelets may appear and form a complex F-wave response. See, for example, FIG. 1, where the F-wave trace A contains two distinct F-wavelets, a=d+e, and only the earliest TOA (i.e., the TOA of F-wavelet d) defines the FWL for the whole F-wave (i.e., the FWL of the complete F-wave complex in trace A). Similarly, and still looking at FIG. 1, F-wave trace B contains two distinct F-wavelets, b=c+e, and only the earliest TOA (i.e., the TOA of F-wavelet c) defines the FWL for the whole F-wave (i.e., the FWL of the complete F-wave complex in trace B).
If only FWL is considered, any clinically relevant TOA information contained in later F-wavelets (e.g., the TOA of F-wavelet e in trace A, and the TOA of F-wavelet e in trace B) is effectively lost. This essentially creates a masking effect with respect to the TOA of the later F-wavelets, and hence reduces the F-wave diagnostic sensitivity. One example where such a masking effect results in reduced sensitivity is in the detection of lumbosacral radiculopathies.
Due to the difficulties in accurately determining the TOA of the later F-wavelets in a complete F-wave complex, clinical F-wave analyses have traditionally relied heavily on FWL, and minimized reliance on TOA.
In addition to aforementioned complexities associated with clinical F-waves analyses, and particularly with using clinical F-waves analyses based on TOA, F-waves also exhibit other characteristics which can complicate their use in clinical assessment and analyses. For example, when compared to CMAPs, F-waves appear to have a highly variable morphology and a very low amplitude due to the small and random number of neurons backfiring at any given time. The acquired F-wave signals also contain noise, power-line frequency interference (PFI) and baseline disturbances.
Thus, there is a need for an automated method to accurately identify and extract individual F-wavelets from a complete F-wave complex, determine the TOA for each F-wavelet, and thereafter construct a TOA profile for the patient nerve based on the TOAs of the specific F-wavelets.